The detection of the concentration level of glucose or other analytes in certain individuals may be vitally important to their health. For example, the monitoring of glucose levels is particularly important to individuals with diabetes or pre-diabetes. People with diabetes may need to monitor their glucose levels to determine when medication (e.g., insulin) is needed to reduce their glucose levels or when additional glucose is needed.
Devices have been developed for automated in vivo monitoring of analyte time series characteristics, such as glucose levels, in bodily fluids such as in the blood stream or in interstitial fluid. Some of these analyte level measuring devices are configured so that at least a portion of the devices are positioned below a skin surface of a user, e.g., in a blood vessel or in the subcutaneous tissue of a user. As used herein, the term analyte monitoring system is used to refer to any type of in vivo monitoring system that uses a sensor disposed with at least a portion subcutaneously to measure and store sensor data representative of analyte concentration levels automatically over time. Analyte monitoring systems include both (1) systems such as continuous glucose monitors (CGMs) which transmit sensor data continuously or at regular time intervals (e.g., once per minute) to a processor/display unit and (2) systems that transfer stored sensor data in one or more batches in response to a request from a processor/display unit (e.g., based on an activation action and/or proximity using a near field communications protocol).
In some cases, analyte monitoring systems have been found to occasionally provide false low readings for relatively short periods (e.g., non-zero-mean signal artifacts). These false low readings, referred to as “dropouts,” are distinct from a situation where no reading at all is provided. When no data at all is provided, an analyte monitoring system can easily detect that there is a problem because there simply is no signal from the sensor. In the case of a dropout however, there is still a signal and the data may appear to be correct but in fact, the data is temporarily incorrect. In a CGM for example, such false data can trigger an unnecessary low blood sugar (e.g., hypoglycemia event) false alarm. Thus, what is needed are systems, methods, and apparatus to reliably determine when a dropout has occurred in analyte monitoring system sensor data.